def test_all_maxima_filtered(self):
self.check_skip()
black_image = np.ones((21, 23)).astype(np.uint8)
draw_point(black_image, [11, 13], 10)
with assert_produces_warning(UserWarning):
f = tp.locate(black_image, 5, minmass=200,
engine=self.engine, preprocess=False)
def test_topn(self):
self.check_skip()
L = 21
dims = (L, L + 2) # avoid square images in tests
cols = ['y', 'x']
PRECISION = 0.1
# top 2
pos1 = np.array([7, 7])
pos2 = np.array([14, 14])
pos3 = np.array([7, 14])
image = np.ones(dims, dtype='uint8')
draw_point(image, pos1, 100)
draw_point(image, pos2, 90)
draw_point(image, pos3, 80)
actual = tp.locate(image, 5, 1, topn=2, preprocess=False,
engine=self.engine)[cols]
actual = actual.sort(['x', 'y']) # sort for reliable comparison
expected = DataFrame([pos1, pos2], columns=cols).sort(['x', 'y'])
assert_allclose(actual, expected, atol=PRECISION)
# top 1
actual = tp.locate(image, 5, 1, topn=1, preprocess=False,
engine=self.engine)[cols]
actual = actual.sort(['x', 'y']) # sort for reliable comparison
expected = DataFrame([pos1], columns=cols).sort(['x', 'y'])
assert_allclose(actual, expected, atol=PRECISION)
def test_subpx_precision(self):
self.check_skip()
L = 21
dims = (L, L + 2) # avoid square images in tests
cols = ['y', 'x']
PRECISION = 0.1
# one bright pixel
pos = [7, 13]
image = np.ones(dims, dtype='uint8')
draw_point(image, pos, 100)
actual = tp.locate(image, 3, 1, preprocess=False,
engine=self.engine)[cols]
expected = DataFrame(np.asarray(pos).reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=0.001) # especially precise
# two neighboring pixels of equal brightness
pos1 = np.array([7, 13])
pos2 = np.array([8, 13])
pos = [7.5, 13] # center is between pixels
image = np.ones(dims, dtype='uint8')
draw_point(image, pos1, 100)
draw_point(image, pos2, 100)
actual = tp.locate(image, 5, 1, preprocess=False,
engine=self.engine)[cols]
expected = DataFrame(np.asarray(pos).reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
# two neighboring pixels of unequal brightness
pos1 = np.array([7, 13])
pos2 = np.array([8, 13])
pos = [7.33, 13] # center is between pixels, biased left
image = np.ones(dims, dtype='uint8')
draw_point(image, pos1, 100)
draw_point(image, pos2, 50)
actual = tp.locate(image, 5, 1, preprocess=False,
engine=self.engine)[cols]
expected = DataFrame(np.asarray(pos).reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos = [7.67, 13] # center is between pixels, biased right
image = np.ones(dims, dtype='uint8')
draw_point(image, pos1, 50)
draw_point(image, pos2, 100)
actual = tp.locate(image, 5, 1, preprocess=False,
engine=self.engine)[cols]
expected = DataFrame(np.asarray(pos).reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos1 = np.array([7, 12])
pos2 = np.array([7, 13])
pos = [7, 12.33] # center is between pixels, biased up
image = np.ones(dims, dtype='uint8')
draw_point(image, pos1, 100)
draw_point(image, pos2, 50)
actual = tp.locate(image, 5, 1, preprocess=False,
engine=self.engine)[cols]
expected = DataFrame(np.asarray(pos).reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos = [7, 12.67] # center is between pixels, biased down
image = np.ones(dims, dtype='uint8')
draw_point(image, pos1, 50)
draw_point(image, pos2, 100)
actual = tp.locate(image, 5, 1, preprocess=False,
engine=self.engine)[cols]
expected = DataFrame(np.asarray(pos).reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
# four neighboring pixels of unequal brightness
pos1 = np.array([7, 12])
pos2 = np.array([8, 12])
pos3 = np.array([7, 13])
pos4 = np.array([8, 13])
pos = [7.33, 12.5] # center is between pixels, biased left
image = np.ones(dims, dtype='uint8')
draw_point(image, pos1, 100)
draw_point(image, pos2, 50)
draw_point(image, pos3, 100)
draw_point(image, pos4, 50)
actual = tp.locate(image, 5, 1, preprocess=False,
engine=self.engine)[cols]
expected = DataFrame(np.asarray(pos).reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos = [7.67, 12.5] # center is between pixels, biased right
image = np.ones(dims, dtype='uint8')
draw_point(image, pos1, 50)
draw_point(image, pos2, 100)
draw_point(image, pos3, 50)
draw_point(image, pos4, 100)
actual = tp.locate(image, 5, 1, preprocess=False,
engine=self.engine)[cols]
expected = DataFrame(np.asarray(pos).reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos = [7.5, 12.33] # center is between pixels, biased up
image = np.ones(dims, dtype='uint8')
draw_point(image, pos1, 100)
draw_point(image, pos2, 100)
draw_point(image, pos3, 50)
draw_point(image, pos4, 50)
actual = tp.locate(image, 5, 1, preprocess=False,
engine=self.engine)[cols]
expected = DataFrame(np.asarray(pos).reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
pos = [7.5, 12.67] # center is between pixels, biased down
image = np.ones(dims, dtype='uint8')
draw_point(image, pos1, 50)
draw_point(image, pos2, 50)
draw_point(image, pos3, 100)
draw_point(image, pos4, 100)
actual = tp.locate(image, 5, 1, preprocess=False,
engine=self.engine)[cols]
expected = DataFrame(np.asarray(pos).reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=PRECISION)
def test_flat_peak(self):
# This tests the part of locate_maxima that eliminates multiple
# maxima in the same mask area.
self.check_skip()
image = np.ones((21, 23)).astype(np.uint8)
image[11, 13] = 100
image[11, 14] = 100
image[12, 13] = 100
count = len(tp.locate(image, 5, preprocess=False,
engine=self.engine))
self.assertEqual(count, 1)
image = np.ones((21, 23)).astype(np.uint8)
image[11:13, 13:15] = 100
count = len(tp.locate(image, 5, preprocess=False,
engine=self.engine))
self.assertEqual(count, 1)
image = np.ones((21, 23)).astype(np.uint8)
image[11, 13] = 100
image[11, 14] = 100
image[11, 15] = 100
count = len(tp.locate(image, 5, preprocess=False,
engine=self.engine))
self.assertEqual(count, 1)
# This tests that two nearby peaks are merged by
# picking the one with the brighter neighborhood.
image = np.ones((21, 23)).astype(np.uint8)
pos = [14, 14]
draw_point(image, [11, 13], 100)
draw_point(image, [11, 14], 100)
draw_point(image, [11, 15], 100)
draw_point(image, [14, 13], 101)
draw_point(image, [14, 14], 101)
draw_point(image, [14, 15], 101)
cols = ['y', 'x']
actual = tp.locate(image, 5, preprocess=False,
engine=self.engine)[cols]
expected = DataFrame(np.asarray(pos).reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=0.1)
# Break ties by sorting by position, simply to avoid
# any randomness resulting from cKDTree returning a set.
image = np.ones((21, 23)).astype(np.uint8)
pos = [14, 14]
draw_point(image, [11, 12], 100)
draw_point(image, [11, 13], 100)
draw_point(image, [11, 14], 100)
draw_point(image, [14, 13], 100)
draw_point(image, [14, 14], 100)
draw_point(image, [14, 15], 100)
cols = ['y', 'x']
actual = tp.locate(image, 5, preprocess=False,
engine=self.engine)[cols]
expected = DataFrame(np.asarray(pos).reshape(1, -1), columns=cols)
assert_allclose(actual, expected, atol=0.1)
def test_maxima_in_margin_3D(self):
self.check_skip()
black_image = np.ones((21, 23, 25)).astype(np.uint8)
draw_point(black_image, [1, 1, 1], 100)
with assert_produces_warning(UserWarning):
f = tp.locate(black_image, 5, engine=self.engine)
